Various imaging systems and tools have been developed to assist physicians, clinicians, radiologists, etc. in evaluating medical images to diagnose medical conditions. For example, computer-aided detection (CAD) tools have been developed for various clinical applications to provide automated detection of medical conditions in medical images, such as colonic polyps and other abnormal anatomical structures such as lung nodules, lesions, aneurysms, calcification, in breast, heart or artery tissue, etc.
Magnetic resonance imaging (MRI) is a medical imaging technique that uses a powerful magnetic field to image the internal structure and certain functionality of a body. MRI is particularly suited for imaging soft tissue structures and is thus highly useful in the field of oncology for the detection of lesions.
Dynamic contrast-enhanced MRI (DCE-MRI) allows for many additional details pertaining to bodily soft tissue to be observed, to further aid in diagnosis and treatment of detected lesions. DCE-MRI may be performed by acquiring a sequence of magnetic resonance (MR) images that span a time before magnetic contrast agents are introduced into the patient's body and a time after the magnetic contrast agents are introduced. By imaging the patient's body sequentially, a set of images may be acquired that illustrate how the magnetic contrast agent is absorbed and washed out from various portions of the patient's body. This absorption and wash-out information may be used to characterize various internal structures within the body and provide additional diagnostic information.
However, when imaging the breast or other parts of the body for the purpose of performing computer-aided detection of potential lesions, it may be beneficial to first segment the acquired medical image data. Segmentation is the process of determining the contour delineating the region of interest from the remainder of the image. Segmentation of lesions in MRI analysis is important not only for measuring the size but also for measuring the morphological properties of lesions that are directly related to their malignancy. For example, a lesion with spikes (called spiculation) is more likely to be malignant than a lesion with a smooth and round shape.
In a CAD system, a lesion is segmented based on the amount of enhancement caused by the contrast agent. However, differences in the amount of enhancement inside a lesion may make segmentation difficult, thereby complicating the search for potential lesions. In addition, segmentation and morphological measurement typically presents a chicken-and-egg problem. Inaccurate segmentation often leads to incorrect morphological measurement of lesions. However, correct morphological measurement is required for accurate segmentation. For example, a gland with a thin sheet shape no longer looks like a thin sheet when it is merged with an oval benign lesion. The oval benign lesion may look malignant if the gland attached to it is confused as its spiculation. Correct morphological measurement of each part is necessary to detach the gland from the oval lesion.
Therefore, there is a need for a new methodology that effectively addresses these problems.